The importance of local epidemic conditions in monitoring progress towards HIV epidemic control in Kenya: a modelling study

Abstract

Introduction: Setting and monitoring progress towards targets for HIV control is critical in ensuring responsive programmes. Here, we explore how to apply targets for reduction in HIV incidence to local settings and which indicators give the strongest signal of a change in incidence in the population and are therefore most important to monitor.

Methods: We use location-specific HIV transmission models, tailored to the epidemics in the counties and major cities in Kenya, to project a wide range of plausible future epidemic trajectories through varying behaviours, treatment coverage and prevention interventions. We look at the change in incidence across modelled scenarios in each location between 2015 and 2030 to inform local target setting. We also simulate the measurement of a library of potential indicators and assess which are most strongly associated with a change in incidence.

Results: Considerable variation was observed in the trajectory of the local epidemics under the plausible scenarios defined (only 10 of 48 locations saw a median reduction in incidence of greater than or equal to an 80% target by 2030). Indicators that provide strong signals in certain epidemic types may not perform consistently well in settings with different epidemiological features. Predicting changes in incidence is more challenging in advanced generalized epidemics compared to concentrated epidemics where changes in high-risk sub-populations track more closely to the population as a whole. Many indicators demonstrate only limited association with incidence (such as "condom use" or "pre-exposure prophylaxis coverage"). This is because many other factors (low effectiveness, impact of other interventions, countervailing changes in risk behaviours, etc.) can confound the relationship between interventions and their ultimate long-term impact, especially for an intervention with low expected coverage. The population prevalence of viral suppression shows the most consistent associations with long-term changes in incidence even in the largest generalized epidemics.

Conclusions: Target setting should be appropriate for the local epidemic and what can feasibly be achieved. There is no one universally reliable indicator to predict future HIV incidence across settings. Thus, the signature of epidemic control must contain indications of success across a wide range of interventions and outcomes.

Keywords: HIV Infections; epidemiology; forecasting; models; population surveillance; programme evaluation; theoretical.

Figure 1. Classification of the forty‐eight modelled locations into five epidemic types

This figure describes the classification of counties according to their epidemiological characteristics for each location (horizontal axis) of the five epidemic types (groups 1 to 5 delineated with vertical white lines). The top panel shows the PAF by sub‐population (vertical axis) across locations (horizontal axis) in each epidemic group and the bottom panel shows the HIV prevalence across locations (horizontal axis) in each epidemic group. Here, groups 1 to 3 represent those epidemics with a relatively high dependency on transmission from high‐risk groups. Group 1 represents those epidemics with high PAF values in both general and high‐risk populations. Group 2 represents those epidemics with large PAF values for MSM and group 3 represents FSW‐driven epidemics. Groups 4 and 5 describe those epidemics with lower contributions from higher risk groups and a greater dependency on the general population. Group 4 represents more established epidemics and very high prevalence settings. Group 5 represents those epidemics with most transmission in the general population but with generally lower population prevalence than in group 4. FSW, female sex workers; MSM, men who have sex with men; PAF, population attributable fraction.

Figure 2. Change in incidence observed by 2030 from 2015 levels for each modelled location (left panel) and nationally (Right Panel)

Each line corresponds to a different location, with the maximum and minimum value corresponding to the maximum and minimum change in incidence between 2015 and 2030 across the modelled future scenarios (the universe of plausible projections). The colour of each plotted location corresponds to the epidemic group it belongs to. Here, groups 1 to 3 represent those epidemics with a relatively high dependency on transmission from high‐risk groups. Group 1 represents those epidemics with high PAF values in both general and high‐risk populations. Group 2 represents those epidemics with large PAF values for MSM and group 3 represents FSW‐driven epidemics. Groups 4 and 5 describe those epidemics with lower contributions from higher risk groups and a greater dependency on the general population. Group 4 represents more established epidemics and very high prevalence settings. Group 5 represents those epidemics with most transmission in the general population but with generally lower population prevalence than in group 4. FSW, female sex workers; MSM, men who have sex with men; PAF, Population Attributable Fraction.

Figure 3. Strength of each indicator in predicting long‐term changes in incidence for each epidemic type

Dark colours are those indicators with low adjusted R² values (blue), light colours are those indicators with high adjusted R² values and that perform much more strongly (yellow). Group 1 represents those epidemics with high PAF values in both general and high‐risk populations. Group 2 represents those epidemics with large PAF values for MSM and group 3 represents FSW‐driven epidemics. Groups 4 and 5 describe those epidemics with lower contributions from higher risk groups and a greater dependency on the general population. Group 4 represents more established epidemics and very high prevalence settings. Group 5 represents those epidemics with most transmission in the general population but with generally lower population prevalence than in group 4. FSW, female sex workers; PAF, Population Attributable Fraction; MSM, men who have sex with men.